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Build, train and deploy ML models with Amazon SageMaker (May 2019)
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Talk @ AWS Summit Stockholm, with HID Global - 22/05/2019
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Build, train and deploy ML models with Amazon SageMaker (May 2019)
1.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build, train and deploy Machine Learning models on Amazon SageMaker Julien Simon Global Evangelist, AI & Machine Learning @julsimon Gàbor Stikkel Senior Data Scientist HID Global
2.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker 1 2 3
3.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build, train and deploy models using SageMaker Business Problem ML problem framing Data collection Data integration Data preparation and cleaning Data visualization and analysis Feature engineering Model training and parameter tuning Model evaluation Monitoring and debugging Model deployment Predictions Are business goals met? YESNO Dataaugmentation Feature augmentation Re-training Neo Elastic inference Ground Truth
4.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T The HID Global's Twist Contest Gesture Recognition in Access Control AWS Summit Stockholm, 22 May, 2019 Gábor Stikkel, Senior Data Scientist, HID Global
5.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T ASSA ABLOY named in Forbes Top 100 of the World’s Most Innovative Companies Every day millions of people in more than 100 countries use our products and services to securely access physical and digital places Over 2 billion things that need to be identified, verified and tracked are connected through HID’s technology 3,200+ employees worldwide Part of ASSA ABLOY: 47000+
6.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 6
7.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Data-driven use cases in physical access control Seamless Access Tap Twist and Go
8.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Launch of Mobile Services was a success Number of openings
9.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Data collection
10.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Business problem • Simple threshold based rule • Many different behaviours • Security issues Goal: reduce false positives whilst providing a delightful experience
11.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Predictive Modeling Pipeline Data batches Features Amazon SageMaker Amazon S3 Notebooks Core ML
12.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Feature calculation using sliding windows Progammable Gesture Recognition for Augmenting Assistive Devices Sishir Patil at. al. 2018 for details)
13.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Predictive modeling Neural networks • reproducability issues • many parameters for even simple models Tree based ensembles • better performance • smaller footprint
14.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Reducing false positives Extra improvement: twist is recognized ~275ms earlier
15.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T ? Tree picture is from https://www.esat.kuleuven.be/Phone picture is from https://uae.souq.com/
16.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T First opening based on a ML model! 2019-01-18 14:23:43.289 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.0010217684 2019-01-18 14:23:43.306 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.002618488 2019-01-18 14:23:43.326 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.0030016804 2019-01-18 14:23:43.345 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.0070577543 2019-01-18 14:23:43.363 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.027276957 2019-01-18 14:23:43.384 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.064488105 2019-01-18 14:23:43.404 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.06967241 2019-01-18 14:23:43.421 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.07492348 2019-01-18 14:23:43.443 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.079435736 2019-01-18 14:23:43.461 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.3511875 2019-01-18 14:23:43.479 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Prediction 0.5662894 2019-01-18 14:23:43.480 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Opening! 2019-01-18 14:23:43.481 17694-17694/com.assaabloy.mobilekeys.android.v2 D/c.a.m.a.e.b.TwistAndGoUltraOpeningTrigger: [main] Twist and Go detected
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Productification - Android • treelite to compile the Python model into a C function • AWS: Neo AI to accelerate model deployment to edge devices • Keeping feature calculation in synch is hard
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Productification - iOS • More black-box than Android • Same challenges with feature calculation
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Conclusions Data is the new water – it comes from every tap People are unpredictable – they invent all sorts of gestures Lowest hanging fruits are grown on decision trees ”ML tool support from AWS making data scientists' life easier”
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Isaac Newton 1643 - 1727 Alessandro Volta 1745 - 1827 André-Marie Ampère 1775 - 1836 Georg Ohm 1789 - 1854 C-A. de Coulomb 1736 - 1806 Michael Faraday 1791 - 1867 Joseph Henry 1797 - 1878 Harvey Nathanson 1936 - János Neumann 1903 - 1957 Arthur Samuel 1901 - 1990 Transistor 1926 - Thomas Edison 1847 - 1931 Nikolas Tesla 1856 - 1943 Ada Lovelace 1815 - 1852 Jack Kilby 1923 - 2005 Robert Noyce 1927 - 1990 McCulloch - Pitts 1943 Steve Jobs 1955 - 2011 Leo Breiman 1928 - 2005 Tianqi Chen Pictures imported from wikipedia.org
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T The five beer team
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S U M
M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
23.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Model options Training code Factorization Machines Linear Learner Principal Component Analysis K-Means Clustering XGBoost And more Built-in Algorithms (17) No ML coding required No infrastructure work required Distributed training Pipe mode Bring Your Own Container Full control, run anything! R, C++, etc. No infrastructure work required Built-in Frameworks Bring your own code: script mode Open source containers No infrastructure work required Distributed training Pipe mode
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Built-in Deep Learning frameworks: just add your code • Built-in containers for training and prediction. • Code available on Github, e.g. https://github.com/aws/sagemaker-tensorflow-containers • Build them, run them on your own machine, customize them, etc. • Script mode: use the same code as on your laptop No infrastructure work required: simply define instance type and instance count Distributed training out of the box: zero setup Pipe mode: stream infinitely large datasets directly from Amazon S3
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS: The platform of choice to run TensorFlow 85% of all TensorFlow workloads in the cloud runs on AWS
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Training ResNet-50 with the ImageNet dataset using our optimized build of Tensorflow 1.11 on a c5.18xlarge instance type is 11x faster than training on the stock binaries. Optimizing Tensorflow for Amazon EC2 instances C5 instances (Intel Skylake) 65% 90% P3 instances (NVIDIA V100)
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T ApacheMXNet:DeepLearningforenterprisedevelopers • Gluon CV Gluon NLP ONNX 2x faster Java Scala
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Demo: Keras+Tensorflow Script mode Automatic model tuning Elastic inference https://gitlab.com/juliensimon/dlnotebooks/tree/master/keras/04-fashion- mnist-sagemaker-advanced
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© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Getting started http://aws.amazon.com/free https://aws.amazon.com/sagemaker https://github.com/aws/sagemaker-python-sdk https://github.com/awslabs/amazon-sagemaker-examples https://medium.com/@julsimon https://gitlab.com/juliensimon/dlnotebooks
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Thank you! S U
M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Julien Simon Global Evangelist, AI & Machine Learning @julsimon Gàbor Stikkel Senior Data Scientist HID Global
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S U M
M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
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